The DOI matches its Crossref record; the references it cites are not checked yet.
Supervisory model predictive control for combined electrical and thermal supply with multiple sources and storages
Abstract
Abstract We present the design, implementation, and experimental validation of a supervisory model predictive control approach for the simultaneous optimization of thermal and electrical building energy supplies. The system comprises a heat pump, a photovoltaic system, an electric battery, and a thermal storage tank. The system uses a phase change slurry as both the heat storage and heat transfer medium, which, beyond an increased storage capacity, results in an improved performance of the heat pump and an increased load-shifting potential. In contrast to approaches that use volumetric flow rates as inputs, the supervisory model predictive control used here determines optimal energy flows. As a result, the online optimization problems do not require impractical computational power in spite of the system complexity. We show the control can be implemented on low-cost embedded hardware and validate it with an experimental demonstrator comprising an installation of the complete supply system, including all hydraulic and all electrical components. Experimental results demonstrate the feasibility of both, the heat pump based heating system with a phase change slurry, and the optimal control approach. The main control objectives, i.e., thermal comfort and maximum self-consumption of solar energy, can be met. In addition, the system and its controller provide a load shifting potential.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
The citation lists haven’t been fetched yet
Cited by 11
Research works printed but not linked: 24
- Kolokotsa, 2011, A roadmap towards intelligent net zero-and positive-energy buildings, Sol Energy, 85, 3067, 10.1016/j.solener.2010.09.001 · DOI
- Afram, 2014, Theory and applications of HVAC control systems–A review of model predictive control (MPC), Build Environ, 72, 343, 10.1016/j.buildenv.2013.11.016 · DOI · Paper page
- Shaikh, 2014, A review on optimized control systems for building energy and comfort management of smart sustainable buildings, Renew Sustain Energy Rev, 34, 409, 10.1016/j.rser.2014.03.027 · DOI
- Sevilla, 2015, Advanced control of energy storage systems for PV installation maximizing self-consumption, IFAC-PapersOnLine, 48, 524, 10.1016/j.ifacol.2015.12.433 · DOI
- Bianchini, 2016, Demand-response in building heating systems: A Model Predictive control approach, Appl Energy, 168, 159, 10.1016/j.apenergy.2016.01.088 · DOI
- Thieblemont, 2017, Predictive control strategies based on weather forecast in buildings with energy storage system: A review of the state-of-the art, Energy Build, 153, 485, 10.1016/j.enbuild.2017.08.010 · DOI
- Široky, 2011, Experimental analysis of model predictive control for an energy efficient building heating system, Appl Energy, 88, 3079, 10.1016/j.apenergy.2011.03.009 · DOI
- Hilliard, 2017, Experimental implementation of whole building MPC with zone based thermal comfort adjustments, Build Environ, 125, 326, 10.1016/j.buildenv.2017.09.003 · DOI
- Ma, 2014, Application of economic MPC to the energy and demand minimization of a commercial building, J Process Control, 24, 1282, 10.1016/j.jprocont.2014.06.011 · DOI
- Fiorentini, 2017, Hybrid model predictive control of a residential HVAC system with on-site thermal energy generation and storage, Appl Energy, 187, 465, 10.1016/j.apenergy.2016.11.041 · DOI
- Shao, 2015, Review of phase change emulsions (PCMEs) and their applications in HVAC systems, Energy Build, 94, 200, 10.1016/j.enbuild.2015.03.003 · DOI
- Delgado, 2012, Review on phase change material emulsions and microencapsulated phase change material slurries: Materials, heat transfer studies and applications, Renew Sustain Energy Rev, 16, 253, 10.1016/j.rser.2011.07.152 · DOI
- Huang, 2009, Evaluation of paraffin/water emulsion as a phase change slurry for cooling applications, Energy, 34, 1145, 10.1016/j.energy.2009.03.016 · DOI
- Löhr, 2017, Domestic heat generation and distribution with time-variant receding horizon control, IFAC-PapersOnLine, 50, 4191, 10.1016/j.ifacol.2017.08.810 · DOI
- Löhr, 2018, Optimal operation of electrical heating system with hybrid model predictive control, IFAC-PapersOnLine, 51, 274, 10.1016/j.ifacol.2018.11.714 · DOI
- Huang, 2010, Low temperature paraffin phase change emulsions, Int J Refrig, 33, 1583, 10.1016/j.ijrefrig.2010.05.016 · DOI
- Mei, 2017, Energy-efficient predictive control of indoor thermal comfort and air quality in a direct expansion air conditioning system, Appl Energy, 195, 439, 10.1016/j.apenergy.2017.03.076 · DOI
- Barlow JS. Data-based predictive control with multirate prediction step. In: Proceedings of the 2010 American control conference. 2010. p. 5513–9. · DOI
- Bemporad, 1999, Control of systems integrating logic, dynamics, and constraints, Automatica, 35, 407, 10.1016/S0005-1098(98)00178-2 · DOI
- Oldewurtel, 2013, Importance of occupancy information for building climate control, Appl Energy, 101, 521, 10.1016/j.apenergy.2012.06.014 · DOI
- Rahman, 2018, Predicting heating demand and sizing a stratified thermal storage tank using deep learning algorithms, Appl Energy, 228, 108, 10.1016/j.apenergy.2018.06.064 · DOI
- Guo, 2018, Machine learning-based thermal response time ahead energy demand prediction for building heating systems, Appl Energy, 221, 16, 10.1016/j.apenergy.2018.03.125 · DOI
- Wächter, 2006, On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming, Math Program, 106, 25, 10.1007/s10107-004-0559-y · DOI · Paper page
- Moshövel, 2015, Analysis of the maximal possible grid relief from PV-peak-power impacts by using storage systems for increased self-consumption, Appl Energy, 137, 567, 10.1016/j.apenergy.2014.07.021 · DOI
Metadata
- Authors
- Löhr, Y., Wolf, D., Pollerberg, C., Hörsting, A., & Mönnigmann, M.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 290
- Pages
- 116742
- ISSN
- 0306-2619
- Cited by
- 11
- Language
- en
- Licence
- Funders
- Bundesministerium fur Wirtschaft und Energie
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Supervisory model predictive control for combined electrical and thermal supply with multiple sources and storages. https://app.cochat.ai/papers/10.1016/j.apenergy.2021.116742
- The paper
- Löhr, Y., Wolf, D., Pollerberg, C., Hörsting, A., & Mönnigmann, M. (2021). Supervisory model predictive control for combined electrical and thermal supply with multiple sources and storages. Applied Energy. https://doi.org/10.1016/j.apenergy.2021.116742
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.